Introduction

Setup

You should have R installed – if not: Open a web browser and go to http://cran.r-project.org and download and install it.

Also helpful to install RStudio (download from http://rstudio.com)

In R, type install.packages("ggplot2") to install the ggplot2 package.

Let’s look at some data

cpi <- read.csv("Datasets/CPI-data.csv")
head(cpi[1:5])
##   X2016.Rank     Country X2016.Score X2015.Score X2014.Score
## 1          1     Denmark          90          91          92
## 2          1 New Zealand          90          88          91
## 3          3     Finland          89          90          89
## 4          4      Sweden          88          89          87
## 5          5 Switzerland          86          86          86
## 6          6      Norway          85          87          86
housing <- read.csv("Datasets/landdata-states.csv")
head(cpi[1:5])
##   X2016.Rank     Country X2016.Score X2015.Score X2014.Score
## 1          1     Denmark          90          91          92
## 2          1 New Zealand          90          88          91
## 3          3     Finland          89          90          89
## 4          4      Sweden          88          89          87
## 5          5 Switzerland          86          86          86
## 6          6      Norway          85          87          86

ggplot2 versus Base Graphics

Compared to base graphics, ggplot2

is more verbose for simple / canned graphics is less verbose for complex / custom graphics does not have methods (data should always be in a data.frame) uses a different system for adding plot elements

ggplot2 versus Base for simple graphs

Base graphics histogram example:

hist(housing$Home.Value)

ggplot2 histogram example:

library(ggplot2)
ggplot(cpi, aes(x = X2016.Score)) +
  geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 2 rows containing non-finite values (stat_bin).

ggplot(housing, aes(x = Home.Value)) +
  geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Clearly Base graphics histogram looks better and cleaner. Therefore Base graph wins!

ggplot2 versus Base for complex graphs

Base graphics histogram example:

plot(Home.Value ~ Date,
     data=subset(housing, State == "MA"))
points(Home.Value ~ Date, col="red",
       data=subset(housing, State == "TX"))
legend(1975, 400000,
       c("MA", "TX"), title="State",
       col=c("black", "red"),
       pch=c(1, 1))

ggplot2 versus Base for complex graphs

ggplot2 color scatter plot example:

ggplot(subset(housing, State %in% c("MA", "TX")),
       aes(x=Date, y=Home.Value, color=State)) +
  geom_point()

Clearly ggplot2 color scatter plot looks better and cleaner. Therefore ggplot2 graph wins!

Geometric Objects And Aesthetics

Aesthetic Mapping (aes)

In ggplot land aesthetic means “something you can see”. Examples include:

  • position (i.e., on the x and y axes)
  • color (“outside” color)
  • fill (“inside” color)
  • shape (of points)
  • linetype
  • size Each type of geom accepts only a subset of all aesthetics–refer to the geom help pages to see what mappings each geom accepts. Aesthetic mappings are set with the aes() function.

Geometic Objects (geom)

Geometric objects are the actual marks we put on a plot. Examples include:

  • points (geom_point, for scatter plots, dot plots, etc)
  • lines (geom_line, for time series, trend lines, etc)
  • boxplot (geom_boxplot, for, well, boxplots!) A plot must have at least one geom; there is no upper limit. You can add a geom to a plot using the + operator

You can get a list of available geometric objects using the code below:

help.search("geom_", package = "ggplot2")

or simply type geom_ in any good R IDE (such as Rstudio or ESS) to see a list of functions starting with geom_.

Points (Scatterplot)

Now that we know about geometric objects and aesthetic mapping, we can make a ggplot. geom_point requires mappings for x and y, all others are optional.

hp2001Q1 <- subset(housing, Date == 2001.25) 
ggplot(hp2001Q1, aes(y = Structure.Cost, x = Land.Value)) +
  geom_point()

For a better view of the underlying structure of the data - use log.

ggplot(hp2001Q1, aes(y = Structure.Cost, x = log(Land.Value))) +
  geom_point()

Lines (Prediction Line)

A plot constructed with ggplot can have more than one geom. In that case the mappings established in the ggplot() call are plot defaults that can be added to or overridden. Our plot could use a regression line:

hp2001Q1$pred.SC <- predict(lm(Structure.Cost ~ log(Land.Value), data = hp2001Q1))

p1 <- ggplot(hp2001Q1, aes(x = log(Land.Value), y = Structure.Cost))

p1 + geom_point(aes(color = Home.Value)) +
  geom_line(aes(y = pred.SC))

Smoothers

Not all geometric objects are simple shapes–the smooth geom includes a line and a ribbon. Note: geom_smooth() by default uses method = ‘loess’

p1 +
  geom_point(aes(color = Home.Value)) +
  geom_smooth()
## `geom_smooth()` using method = 'loess'

Text (Label Points)

Each geom accepts a particualar set of mappings–for example geom_text() accepts a labels mapping.

p1 + 
  geom_text(aes(label=State), size = 3)

But what if you want to see both points and text labels?

## install.packages("ggrepel") 
library("ggrepel")
p1 + 
  geom_point() + 
  geom_text_repel(aes(label=State), size = 3)

Aesthetic Mapping VS Assignment

Note that variables are mapped to aesthetics with the aes() function, while fixed aesthetics are set outside the aes() call. This sometimes leads to confusion, as in this example:

p1 +
  geom_point(aes(size = 2), # incorrect! 2 is not a variable
             color="red")   # this is fine -- all points red

Mapping Variables To Other Aesthetics

Other aesthetics are mapped in the same way as x and y in the previous example.

p1 +
  geom_point(aes(color=Home.Value, shape = region))
## Warning: Removed 1 rows containing missing values (geom_point).

Exercise I

The data for the exercises is available in the dataSets/EconomistData.csv file. Read it in with

dat <- read.csv("dataSets/EconomistData.csv")
head(dat)
##   X     Country HDI.Rank   HDI CPI            Region
## 1 1 Afghanistan      172 0.398 1.5      Asia Pacific
## 2 2     Albania       70 0.739 3.1 East EU Cemt Asia
## 3 3     Algeria       96 0.698 2.9              MENA
## 4 4      Angola      148 0.486 2.0               SSA
## 5 5   Argentina       45 0.797 3.0          Americas
## 6 6     Armenia       86 0.716 2.6 East EU Cemt Asia
ggplot(dat, aes(x = CPI, y = HDI, size = HDI.Rank)) + geom_point()

dat <- read.csv(“dataSets/EconomistData.csv”)

Original sources for these data are http://www.transparency.org/content/download/64476/1031428 http://hdrstats.undp.org/en/indicators/display_cf_xls_indicator.cfm?indicator_id=103106&lang=en

These data consist of Human Development Index and Corruption Perception Index scores for several countries.

  1. Create a scatter plot with CPI on the x axis and HDI on the y axis.
  2. Color the points blue.
  3. Map the color of the the points to Region.
  4. Make the points bigger by setting size to 2
  5. Map the size of the points to HDI.Rank
  6. Create boxplots of CPI by Region
  7. Overlay points on top of the box plots

Exercise I - Answers

dat <- read.csv("dataSets/EconomistData.csv")
head(dat)
##   X     Country HDI.Rank   HDI CPI            Region
## 1 1 Afghanistan      172 0.398 1.5      Asia Pacific
## 2 2     Albania       70 0.739 3.1 East EU Cemt Asia
## 3 3     Algeria       96 0.698 2.9              MENA
## 4 4      Angola      148 0.486 2.0               SSA
## 5 5   Argentina       45 0.797 3.0          Americas
## 6 6     Armenia       86 0.716 2.6 East EU Cemt Asia
ggplot(dat, aes(x = CPI, y = HDI)) +
  geom_point()

ggplot(dat, aes(x = CPI, y = HDI)) +
  geom_point(color="blue")

ggplot(dat, aes(x = CPI, y = HDI)) + 
  geom_point(aes(color=Region))

ggplot(dat, aes(x = CPI, y = HDI, size = 2)) + 
  geom_point(aes(color=Region))

ggplot(dat, aes(x = CPI, y = HDI, size = HDI.Rank)) + 
  geom_point(aes(color=Region))

ggplot(dat, aes(x = Region, y = CPI)) +
  geom_boxplot()

ggplot(dat, aes(x = Region, y = CPI)) +
  geom_boxplot() +
  geom_point()

Statistical Transformations

Statistical Transformations

Some plot types (such as scatterplots) do not require transformations–each point is plotted at x and y coordinates equal to the original value. Other plots, such as boxplots, histograms, prediction lines etc. require statistical transformations:

  • for a boxplot the y values must be transformed to the median and 1.5(IQR)
  • for a smoother smother the y values must be transformed into predicted values

Each geom has a default statistic, but these can be changed. For example, the default statistic for geom_bar is stat_bin:

args(geom_histogram)
## function (mapping = NULL, data = NULL, stat = "bin", position = "stack", 
##     ..., binwidth = NULL, bins = NULL, na.rm = FALSE, show.legend = NA, 
##     inherit.aes = TRUE) 
## NULL
args(stat_bin)
## function (mapping = NULL, data = NULL, geom = "bar", position = "stack", 
##     ..., binwidth = NULL, bins = NULL, center = NULL, boundary = NULL, 
##     breaks = NULL, closed = c("right", "left"), pad = FALSE, 
##     na.rm = FALSE, show.legend = NA, inherit.aes = TRUE) 
## NULL

Setting Statistical Transformation Arguments

Arguments to stat_ functions can be passed through geom_ functions. This can be slightly annoying because in order to change it you have to first determine which stat the geom uses, then determine the arguments to that stat.

For example, here is the default histogram of Home.Value:

p2 <- ggplot(housing, aes(x = Home.Value))
p2 + geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

reasonable by default, but we can change it by passing the binwidth argument to the stat_bin function:

p2 + geom_histogram(stat = "bin", binwidth=4000)

Changing The Statistical Transformation

Sometimes the default statistical transformation is not what you need. This is often the case with pre-summarized data:

housing.sum <- aggregate(housing["Home.Value"], housing["State"], FUN=mean)
rbind(head(housing.sum), tail(housing.sum))
##    State Home.Value
## 1     AK  147385.14
## 2     AL   92545.22
## 3     AR   82076.84
## 4     AZ  140755.59
## 5     CA  282808.08
## 6     CO  158175.99
## 46    VA  155391.44
## 47    VT  132394.60
## 48    WA  178522.58
## 49    WI  108359.45
## 50    WV   77161.71
## 51    WY  122897.25
## ggplot(housing.sum, aes(x=State, y=Home.Value)) + 
##   geom_bar()

##Error: stat_count() must not be used with a y aesthetic.

What is the problem with the previous plot? Basically we take binned and summarized data and ask ggplot to bin and summarize it again (remember, geom_bar defaults to stat = stat_count); obviously this will not work. We can fix it by telling geom_bar to use a different statistical transformation function:

ggplot(housing.sum, aes(x=State, y=Home.Value)) + 
  geom_bar(stat="identity")

Exercise II

  1. Re-create a scatter plot with CPI on the x axis and HDI on the y axis (as you did in the previous exercise).
  2. Overlay a smoothing line on top of the scatter plot using geom_smooth.
  3. Overlay a smoothing line on top of the scatter plot using geom_smooth, but use a linear model for the predictions. Hint: see ?stat_smooth.
  4. Overlay a smoothing line on top of the scatter plot using geom_line. Hint: change the statistical transformation.
  5. BONUS: Overlay a smoothing line on top of the scatter plot using the default loess method, but make it less smooth. Hint: see ?loess.

Exercise II - Answers

dat <- read.csv("dataSets/EconomistData.csv")
head(dat)
##   X     Country HDI.Rank   HDI CPI            Region
## 1 1 Afghanistan      172 0.398 1.5      Asia Pacific
## 2 2     Albania       70 0.739 3.1 East EU Cemt Asia
## 3 3     Algeria       96 0.698 2.9              MENA
## 4 4      Angola      148 0.486 2.0               SSA
## 5 5   Argentina       45 0.797 3.0          Americas
## 6 6     Armenia       86 0.716 2.6 East EU Cemt Asia
ggplot(dat, aes(x = CPI, y = HDI)) +
  geom_point()

ggplot(dat, aes(x = CPI, y = HDI)) +
  geom_point() +
  geom_smooth()
## `geom_smooth()` using method = 'loess'

ggplot(dat, aes(x = CPI, y = HDI)) +
  geom_point() +
  geom_smooth(method = "lm")

ggplot(dat, aes(x = CPI, y = HDI)) +
  geom_point() + 
  geom_smooth(method = "lm", se = FALSE)

ggplot(dat, aes(x = CPI, y = HDI)) +
  geom_point() + 
  geom_smooth(method = "glm")

ggplot(dat, aes(x = CPI, y = HDI)) +
  geom_point() + 
  geom_line()

ggplot(dat, aes(x = CPI, y = HDI)) +
  geom_point() + 
  geom_smooth(method = "loess")

ggplot(dat, aes(x = CPI, y = HDI)) +
  geom_point() + 
  geom_smooth(method = "loess", span = 0.3)

ggplot(dat, aes(x = CPI, y = HDI)) +
  geom_point() + 
  geom_smooth(method = "loess", span = 0.5)

ggplot(dat, aes(x = CPI, y = HDI)) +
  geom_point() + 
  geom_smooth(method = "loess", span = 0.8)

Scales

Scales: Controlling Aesthetic Mapping

Aesthetic mapping (i.e., with aes()) only says that a variable should be mapped to an aesthetic. It doesn’t say how that should happen. For example, when mapping a variable to shape with aes(shape = x) you don’t say what shapes should be used. Similarly, aes(color = z) doesn’t say what colors should be used. Describing what colors/shapes/sizes etc. to use is done by modifying the corresponding scale. In ggplot2 scales include

  • position
  • color and fill
  • size
  • shape
  • line type

Scales are modified with a series of functions using a scale_<aesthetic>_<type> naming scheme. Try typing scale_<tab> to see a list of scale modification functions.

Common Scale Arguments

The following arguments are common to most scales in ggplot2:

  • name - the first argument gives the axis or legend title
  • limits - the minimum and maximum of the scale
  • breaks - the points along the scale where labels should appear
  • labels - the labels that appear at each break

Specific scale functions may have additional arguments; for example, the scale_color_continuous function has arguments low and high for setting the colors at the low and high end of the scale.

Scale Shape Modifications

Geoms that draw points have a shape parameter. Legal shape values are

  • The numbers 0 to 25, and
  • The numbers 32 to 127.
  • Only shapes 21 to 25 are filled (and thus are affected by the fill color),
  • The rest are just drawn in the outline color.
  • Shapes 32 to 127 correspond to the corresponding ASCII characters.

For most geoms, the default shape is 16 (a dot). The shape can be set to a constant value or it can be mapped via a scale.

Setting to constant value

To set the shape to a constant value, use the shape geom parameter e.g., geom_point(data=d, mapping=aes(x=x, y=y), shape=3) sets the shape of all points in the layer to 3, which corresponds to a “+”).

Note: The ggplot2 shape parameter corresponds to the pch parameter of the R base graphics package.

Mapping with scale_shape_discrete

Maps upto 6 distinct values to pre-defined shapes. The scale has a boolean option, “solid”, which determines whether the pre-defined set of shapes contains some solid shapes. If solid = T, the first three shapes are solid (but the fourth to sixth shape are hollow).

Note that even though the first three shapes are solid, these three shapes are not actually filled with the fill color (but they are completely drawn in the outline color).

d=data.frame(a=c("a","b","c","d","e","f")) 
ggplot() + scale_x_discrete(name="") +  scale_y_continuous(limits=c(0,1), breaks=NULL, name="") +
    scale_shape_discrete(solid=T, guide=F) + 
    geom_point(data=d, mapping=aes(x=a, y=0.5, shape=a), size=10)

Mapping with scale_shape_identity

The scale_shape_identity scale can be used to pass through any legal shape value (its mapping is the identity function, and thus it does not change anything).

d=data.frame(p=c(0:25,32:127)) 
ggplot() + scale_y_continuous(name="") + 
  scale_x_continuous(name="") + 
  scale_shape_identity() + 
  geom_point(data=d, mapping=aes(x=p%%16, y=p%/%16, shape=p), 
             size=5, fill="red") +
  geom_text(data=d, mapping=aes(x=p%%16, y=p%/%16+0.25, label=p), size=3)

Scale Color Modification Examples

Start by constructing a dotplot showing the distribution of home values by Date and State.

p3 <- ggplot(housing,
             aes(x = State,
                 y = Home.Price.Index)) + 
        theme(legend.position="top",
              axis.text=element_text(size = 6))
(p4 <- p3 + geom_point(aes(color = Date),
                       alpha = 0.5,
                       size = 1.5,
                       position = position_jitter(width = 0.25, height = 0)))

Now modify the breaks for the x axis and color scales

p4 + scale_x_discrete(name="State Abbreviation") +
  scale_color_continuous(name="",
                         breaks = c(1976, 1994, 2013),
                         labels = c("'76", "'94", "'13"))

Next change the low and high values to blue and red:

p4 +
  scale_x_discrete(name="State Abbreviation") +
  scale_color_continuous(name="",
                         breaks = c(1976, 1994, 2013),
                         labels = c("'76", "'94", "'13"),
                         low = "blue", high = "red")

p4 +
  scale_x_discrete(name="State Abbreviation") +
  scale_color_continuous(name="",
                         breaks = c(1976, 1994, 2013),
                         labels = c("'76", "'94", "'13"),
                         low = scales::muted("blue"), high = scales::muted("red"))

Using different color scales

ggplot2 has a wide variety of color scales; here is an example using scale_color_gradient2 to interpolate between three different colors.

p4 +
  scale_color_gradient2(name="",
                        breaks = c(1976, 1994, 2013),
                        labels = c("'76", "'94", "'13"),
                        low = scales::muted("blue"),
                        high = scales::muted("red"),
                        mid = "gray60",
                        midpoint = 1994)

Available Scales

Partial combination matrix of available scales Note that in RStudio you can type scale_ followed by TAB to get the whole list of available scales.

Scale Types Examples
scale_color_ identity scale_fill_continuous_
scale_fill_ manual scale_color_discrete_
scale_size_ continuous scale_size_manual
discrete scale_size_discrete
scale_shape_ discrete scale_shape_discrete
scale_linetype_ identity scale_shape_manual
manual scale_linetype_discrete_
scale_x_ continuous scale_x_continuous_
scale_y_ discrete scale_y_discrete_
reverse scale_x_log_
log scale_y_reverse_
date scale_x_date_
datetime scale_y_datetime_

Exercise III

  1. Create a scatter plot with CPI on the x axis and HDI on the y axis. Color the points to indicate region.
  2. Modify the x, y, and color scales so that they have more easily-understood names (e.g., spell out “Human development Index” instead of “HDI”).
  3. Modify the color scale to use specific values of your choosing. Hint: see ?scale_color_manual.

Exercise III - Answers

dat <- read.csv("dataSets/EconomistData.csv")
head(dat)
##   X     Country HDI.Rank   HDI CPI            Region
## 1 1 Afghanistan      172 0.398 1.5      Asia Pacific
## 2 2     Albania       70 0.739 3.1 East EU Cemt Asia
## 3 3     Algeria       96 0.698 2.9              MENA
## 4 4      Angola      148 0.486 2.0               SSA
## 5 5   Argentina       45 0.797 3.0          Americas
## 6 6     Armenia       86 0.716 2.6 East EU Cemt Asia
ggplot(dat, aes(x = CPI, y = HDI)) +
  geom_point()

ggplot(dat, aes(x = CPI, y = HDI)) + 
  geom_point(aes(color=Region))

ggplot(dat, aes(x = CPI, y = HDI, size = HDI.Rank)) + 
  geom_point(aes(color=Region))

ggplot(dat, aes(x = CPI, y = HDI, size = HDI.Rank)) + 
  geom_point(aes(color=Region)) +
  scale_x_continuous(name="Corruption Perceptions Index") +
  scale_y_continuous(name="Human Development Index")

ggplot(dat, aes(x = CPI, y = HDI, size = HDI.Rank)) + 
  geom_point(aes(color=Region)) +
  scale_x_continuous(name="Corruption Perceptions Index") +
  scale_y_continuous(name="Human Development Index") +
  scale_colour_manual(
    name = "Regions of the World", 
    values = c("red", "blue", "green", "orange", "yellow", "black"))

Faceting

Faceting

  • Faceting is ggplot2 parlance for small multiples
  • The idea is to create separate graphs for subsets of data
  • Facilitates comparison among plots, not just of geoms within a plot
  • ggplot2 offers two functions for creating small multiples:
  1. facet_wrap(): define subsets as the levels of a single grouping variable
  2. facet_grid(): define subsets as the crossing of two grouping variables

What is the trend in housing prices in each state?

Start by using a technique we already know–map State to color:

p5 <- ggplot(housing, aes(x = Date, y = Home.Value))
p5 + geom_line(aes(color = State))

There are two problems here–there are too many states to distinguish each one by color, and the lines obscure one another.

Faceting to the rescue

We can remedy the deficiencies of the previous plot by faceting by state rather than mapping state to color.

(p5 <- p5 + geom_line() +
   facet_wrap(~State, ncol = 10))

There is also a facet_grid() function for faceting in two dimensions.

Themes

Themes

The ggplot2 theme system handles non-data plot elements such as

  • Axis labels
  • Plot background
  • Facet label background
  • Legend appearance

Built-in themes include:

  • theme_gray() (default)
  • theme_bw()
  • theme_classic()
p5 + theme_linedraw()

p5 + theme_light()

Overriding theme defaults

Specific theme elements can be overridden using theme(). For example:

p5 + theme_minimal() +
  theme(text = element_text(color = "turquoise"))

All theme options are documented in ?theme.

Creating and saving new themes

You can create new themes, as in the following example:

theme_new <- theme_bw() +
  theme(plot.background = element_rect(size = 1, color = "blue", fill = "black"),
        text=element_text(color = "ivory"),
        axis.text.y = element_text(colour = "purple"),
        axis.text.x = element_text(colour = "red"),
        panel.background = element_rect(fill = "pink"),
        strip.background = element_rect(fill = scales::muted("orange")))

p5 + theme_new

The #1 FAQ from students

Map Aesthetic To Different Columns

The most frequently asked question goes something like this: I have two variables in my data.frame, and I’d like to plot them as separate points, with different color depending on which variable it is. How do I do that?

Wrong way

housing.byyear <- aggregate(cbind(Home.Value, Land.Value) ~ Date, data = housing, mean)
ggplot(housing.byyear, aes(x=Date)) +
  geom_line(aes(y=Home.Value), color="red") +
  geom_line(aes(y=Land.Value), color="blue")

Right way

library(tidyr)
home.land.byyear <- gather(housing.byyear, value = "value", key = "type", Home.Value, Land.Value)
ggplot(home.land.byyear, aes(x=Date, y=value, color=type)) +
  geom_line()

Putting It All Together

Challenge: Recreate This Economist Graph

Graph source: http://www.economist.com/node/21541178

Building off of the graphics you created in the previous exercises, put the finishing touches to make it as close as possible to the original economist graph.

Challenge - Answers

dat <- read.csv("dataSets/EconomistData.csv")
head(dat)
##   X     Country HDI.Rank   HDI CPI            Region
## 1 1 Afghanistan      172 0.398 1.5      Asia Pacific
## 2 2     Albania       70 0.739 3.1 East EU Cemt Asia
## 3 3     Algeria       96 0.698 2.9              MENA
## 4 4      Angola      148 0.486 2.0               SSA
## 5 5   Argentina       45 0.797 3.0          Americas
## 6 6     Armenia       86 0.716 2.6 East EU Cemt Asia
ggplot(dat, aes(x = CPI, y = HDI, size = HDI.Rank)) + 
  geom_point(aes(color=Region)) +
  scale_x_continuous(name="Corruption Perceptions Index") +
  scale_y_continuous(name="Human Development Index") +
  scale_colour_manual(values = c("red", "blue", "green", "orange", "yellow", "black"))

library("ggrepel")
ggplot(dat, aes(x = CPI, y = HDI, size = HDI.Rank)) + 
  geom_point(shape = 1, aes(color=Region)) +
  geom_text_repel(aes(label=Region), size = 3) + 
  scale_x_continuous(name="Corruption Perceptions Index") +
  scale_y_continuous(name="Human Development Index") +  
  scale_colour_manual(values = c("red", "blue", "green", "orange", "violet", "black"))

Challenge - Answers - Basic

dat <- read.csv("dataSets/EconomistData.csv")

pc1 <- ggplot(dat, aes(x = CPI, y = HDI, color = Region))
pc1 + geom_point()

To complete this graph we need to: * add a trend line * change the point shape to open circle * change the order and labels of Region * label select points * fix up the tick marks and labels * move color legend to the top * title, label axes, remove legend title * theme the graph with no vertical guides * add model R2 (hard) * add sources note (hard) * final touches to make it perfect (use image editor for this)

Adding the trend line

Adding the trend line is not too difficult, though we need to guess at the model being displyed on the graph. A little bit of trial and error leads us to

Notice that we put the geom_line layer first so that it will be plotted underneath the points, as was done on the original graph.

(pc2 <- pc1 +
     geom_smooth(aes(group = 1),
                 method = "lm",
                 formula = y ~ log(x),
                 se = FALSE,
                 color = "red")) +
     geom_point()

Use open circle points

This one is a little tricky. We know that we can change the shape with the shape argument, what value do we set shape to? The example shown in ?shape can help us:

## A look at all 25 symbols
df2 <- data.frame(x = 1:5 , y = 1:25, z = 1:25)
s <- ggplot(df2, aes(x = x, y = y))
s + geom_point(aes(shape = z), size = 4) + scale_shape_identity()

## While all symbols have a foreground colour, symbols 19-25 also take a
## background colour (fill)
s + geom_point(aes(shape = z), size = 4, colour = "Red") +
  scale_shape_identity()

s + geom_point(aes(shape = z), size = 4, colour = "Red", fill = "Black") +
  scale_shape_identity()

This shows us that shape 1 is an open circle, so

pc2 +
  geom_point(shape = 1, size = 4)

That is better, but unfortunately the size of the line around the points is much narrower than on the original. This is a frustrating aspect of ggplot2, and we will have to hack around it. One way to do that is to multiple point layers of slightly different sizes.

(pc3 <- pc2 +
   geom_point(size = 4.5, shape = 1) +
   geom_point(size = 4, shape = 1) +
   geom_point(size = 3.5, shape = 1))

Labelling points

This one is tricky in a couple of ways. First, there is no attribute in the data that separates points that should be labelled from points that should not be. So the first step is to identify those points.

pointsToLabel <- c("Russia", "Venezuela", "Iraq", "Myanmar", "Sudan",
                   "Afghanistan", "Congo", "Greece", "Argentina", "Brazil",
                   "India", "Italy", "China", "South Africa", "Spane",
                   "Botswana", "Cape Verde", "Bhutan", "Rwanda", "France",
                   "United States", "Germany", "Britain", "Barbados",
                   "Norway", "Japan", "New Zealand", "Singapore")

Now we can label these points using geom_text, like this:

(pc4 <- pc3 +
    geom_text(aes(label = Country),
              color = "gray20",
              data = subset(dat, Country %in% pointsToLabel)))

This more or less gets the information across, but the labels overlap in a most unpleasing fashion. We can use the ggrepel package to make things better, but if you want perfection you will probably have to do some hand-adjustment.

library("ggrepel")
pc3 +
  geom_text_repel(aes(label = Country),
            color = "gray20",
            data = subset(dat, Country %in% pointsToLabel),
            force = 10)

Change the region labels and order

Thinkgs are starting to come together. There are just a couple more things we need to add, and then all that will be left are themeing changes. Comparing our graph to the original we notice that the labels and order of the Regions in the color legend differ. To correct this we need to change both the labels and order of the Region variable. We can do this with the factor function.

 dat$Region <- factor(dat$Region,
                       levels = c("EU W. Europe",
                                  "Americas",
                                  "Asia Pacific",
                                  "East EU Cemt Asia",
                                  "MENA",
                                  "SSA"),
                       labels = c("OECD",
                                  "Americas",
                                  "Asia &\nOceania",
                                  "Central &\nEastern Europe",
                                  "Middle East &\nnorth Africa",
                                  "Sub-Saharan\nAfrica"))

Now when we construct the plot using these data the order should appear as it does in the original.

pc4$data <- dat
pc4

Add title and format axes

The next step is to add the title and format the axes. We do that using the scales system in ggplot2.

library(grid)
  (pc5 <- pc4 +
    scale_x_continuous(name = "Corruption Perceptions Index, 2011 (10=least corrupt)",
                       limits = c(.9, 10.5),
                       breaks = 1:10) +
    scale_y_continuous(name = "Human Development Index, 2011 (1=Best)",
                       limits = c(0.2, 1.0),
                       breaks = seq(0.2, 1.0, by = 0.1)) +
    scale_color_manual(name = "",
                       values = c("#24576D",
                                  "#099DD7",
                                  "#28AADC",
                                  "#248E84",
                                  "#F2583F",
                                  "#96503F")) +
    ggtitle("Corruption and Human development"))

Theme tweaks

Our graph is almost there. To finish up, we need to adjust some of the theme elements, and label the axes and legends. This part usually involves some trial and error as you figure out where things need to be positioned. To see what these various theme settings do you can change them and observe the results.

library(grid) # for the 'unit' function
  (pc6 <- pc5 +
    theme_minimal() + # start with a minimal theme and add what we need
    theme(text = element_text(color = "gray20"),
          legend.position = c("top"), # position the legend in the upper left 
          legend.direction = "horizontal",
          legend.justification = 0.1, # anchor point for legend.position.
          legend.text = element_text(size = 11, color = "gray10"),
          axis.text = element_text(face = "italic"),
          axis.title.x = element_text(vjust = -1), # move title away from axis
          axis.title.y = element_text(vjust = 2), # move away for axis
          axis.ticks.y = element_blank(), # element_blank() is how we remove elements
          axis.line = element_line(color = "gray40", size = 0.5),
          axis.line.y = element_blank(),
          panel.grid.major = element_line(color = "gray50", size = 0.5),
          panel.grid.major.x = element_blank()
          ))

Add model R2 and source note

The last bit of information that we want to have on the graph is the variance explained by the model represented by the trend line. Lets fit that model and pull out the R2 first, then think about how to get it onto the graph.

(mR2 <- summary(lm(HDI ~ log(CPI), data = dat))$r.squared)
## [1] 0.5212859

OK, now that we’ve calculated the values, let’s think about how to get them on the graph. ggplot2 has an annotate function, but this is not convenient for adding elements outside the plot area. The grid package has nice functions for doing this, so we’ll use those.

And here it is, our final version!

library(grid)
pc6 
grid.text("Sources: Transparency International; UN Human Development Report",
         x = .02, y = .03,
         just = "left",
         draw = TRUE)
grid.segments(x0 = 0.81, x1 = 0.825,
              y0 = 0.90, y1 = 0.90,
              gp = gpar(col = "red"),
              draw = TRUE)
grid.text(paste0("R² = ",
                 as.integer(mR2*100),
                 "%"),
          x = 0.835, y = 0.90,
          gp = gpar(col = "gray20"),
          draw = TRUE,
          just = "left")

Wrap-up